{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Controlled Anomalies Time Series (CATS) Dataset\n",
    "\n",
    "- Source and description: https://zenodo.org/records/8338435"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "from config import data_raw_folder, data_processed_folder\n",
    "from timeeval import DatasetManager\n",
    "from timeeval.datasets import Datasets, DatasetAnalyzer, DatasetRecord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking for source datasets in /home/projects/akita/data/benchmark-data/data-raw/CATSv2 and\n",
      "saving processed datasets in /home/projects/akita/data/benchmark-data/data-processed\n"
     ]
    }
   ],
   "source": [
    "dataset_collection_name = \"CATSv2\"\n",
    "source_folder = Path(data_raw_folder) / \"CATSv2\"\n",
    "target_folder = Path(data_processed_folder)\n",
    "\n",
    "print(f\"Looking for source datasets in {Path(source_folder).absolute()} and\\nsaving processed datasets in {Path(target_folder).absolute()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created directories /home/projects/akita/data/benchmark-data/data-processed/multivariate/CATSv2\n"
     ]
    }
   ],
   "source": [
    "# shared by all datasets\n",
    "dataset_type = \"real\"\n",
    "input_type = \"multivariate\"\n",
    "datetime_index = True\n",
    "split_at = int(1e6)\n",
    "train_is_normal = True\n",
    "train_type = \"semi-supervised\"\n",
    "\n",
    "# create target directory\n",
    "dataset_subfolder = Path(input_type) / dataset_collection_name\n",
    "target_subfolder = target_folder / dataset_subfolder\n",
    "target_subfolder.mkdir(parents=True, exist_ok=True)\n",
    "print(f\"Created directories {target_subfolder}\")\n",
    "\n",
    "dm = DatasetManager(target_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> Processing source dataset data.csv\n",
      "  skipped writing dataset to disk, because it already exists.\n",
      "  skipped analyzing dataset, because metadata already exists.\n",
      "... processed source dataset: data.csv -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/CATSv2/CATSv2.test.csv\n"
     ]
    }
   ],
   "source": [
    "file = source_folder / \"data.csv\"\n",
    "ignore_stationarity = True\n",
    "\n",
    "print(f\"> Processing source dataset {file.name}\")\n",
    "dataset_name = dataset_collection_name\n",
    "test_filename = f\"{dataset_name}.test.csv\"\n",
    "train_filename = f\"{dataset_name}.train.csv\"\n",
    "test_path = dataset_subfolder / test_filename\n",
    "train_path = dataset_subfolder / train_filename\n",
    "target_test_filepath = target_subfolder / test_filename\n",
    "target_train_filepath = target_subfolder / train_filename\n",
    "target_meta_filepath = target_test_filepath.parent / f\"{dataset_name}.{Datasets.METADATA_FILENAME_SUFFIX}\"\n",
    "\n",
    "# Prepare datasets\n",
    "if not target_test_filepath.exists() or not target_train_filepath.exists() or not target_meta_filepath.exists():\n",
    "    df = pd.read_csv(source_folder / \"data.csv\")\n",
    "    df[\"is_anomaly\"] = df[\"y\"].astype(np.bool_)\n",
    "    df = df.drop(columns=[\"y\", \"category\"])\n",
    "\n",
    "    df_test = df.iloc[split_at:].copy()\n",
    "    df_test.to_csv(target_test_filepath, index=False)\n",
    "\n",
    "    df_train = df_test[:split_at].copy()\n",
    "    df_train.to_csv(target_train_filepath, index=False)\n",
    "    \n",
    "    del df\n",
    "    print(\"  written dataset to disk\")\n",
    "else:\n",
    "    df_test = df_train = None\n",
    "    print(\"  skipped writing dataset to disk, because it already exists.\")\n",
    "\n",
    "# Prepare metadata\n",
    "def analyze(df_test, df_train):\n",
    "    da = DatasetAnalyzer((dataset_collection_name, dataset_name), is_train=False, df=df_test, ignore_stationarity=ignore_stationarity)\n",
    "    da.save_to_json(target_meta_filepath, overwrite=True)\n",
    "    meta = da.metadata\n",
    "    print(\"  analyzed test dataset\")\n",
    "\n",
    "    DatasetAnalyzer((dataset_collection_name, dataset_name), is_train=True, df=df_train, ignore_stationarity=ignore_stationarity)\\\n",
    "        .save_to_json(target_meta_filepath, overwrite=False)\n",
    "    print(\"  analyzed training dataset\")\n",
    "    return meta\n",
    "\n",
    "if target_meta_filepath.exists():\n",
    "    try:\n",
    "        meta = DatasetAnalyzer.load_from_json(target_meta_filepath, train=False)\n",
    "    except ValueError:\n",
    "        if df_test is None:\n",
    "            df_test = pd.read_csv(target_test_filepath)\n",
    "        if df_train is None:\n",
    "            df_train = pd.read_csv(target_train_filepath)\n",
    "        meta = analyze(df_test, df_train)\n",
    "    else:\n",
    "\n",
    "        # check if train metadata is also present\n",
    "        try:\n",
    "            DatasetAnalyzer.load_from_json(target_meta_filepath, train=True)\n",
    "            print(\"  skipped analyzing dataset, because metadata already exists.\")\n",
    "        except ValueError:\n",
    "            if df_train is None:\n",
    "                df_train = pd.read_csv(target_train_filepath)\n",
    "            DatasetAnalyzer((dataset_collection_name, dataset_name), is_train=True, df=df_train, ignore_stationarity=ignore_stationarity)\\\n",
    "                .save_to_json(target_meta_filepath, overwrite=False)\n",
    "            print(\"  analyzed training dataset\")\n",
    "else:\n",
    "    meta = analyze(df_test, df_train)\n",
    "\n",
    "dm.add_dataset(DatasetRecord(\n",
    "      collection_name=dataset_collection_name,\n",
    "      dataset_name=dataset_name,\n",
    "      train_path=train_path,\n",
    "      test_path=test_path,\n",
    "      dataset_type=dataset_type,\n",
    "      datetime_index=datetime_index,\n",
    "      split_at=split_at,\n",
    "      train_type=train_type,\n",
    "      train_is_normal=train_is_normal,\n",
    "      input_type=input_type,\n",
    "      length=meta.length,\n",
    "      dimensions=meta.dimensions,\n",
    "      contamination=meta.contamination,\n",
    "      num_anomalies=meta.num_anomalies,\n",
    "      min_anomaly_length=meta.anomaly_length.min,\n",
    "      median_anomaly_length=meta.anomaly_length.median,\n",
    "      max_anomaly_length=meta.anomaly_length.max,\n",
    "      mean=meta.mean,\n",
    "      stddev=meta.stddev,\n",
    "      trend=meta.trend,\n",
    "      stationarity=meta.get_stationarity_name(),\n",
    "      period_size=np.nan\n",
    "))\n",
    "print(f\"... processed source dataset: {file.name} -> {target_test_filepath}\")\n",
    "\n",
    "dm.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>median_anomaly_length</th>\n",
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       "      <th>mean</th>\n",
       "      <th>stddev</th>\n",
       "      <th>trend</th>\n",
       "      <th>stationarity</th>\n",
       "      <th>period_size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collection_name</th>\n",
       "      <th>dataset_name</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CATSv2</th>\n",
       "      <th>CATSv2</th>\n",
       "      <td>multivariate/CATSv2/CATSv2.train.csv</td>\n",
       "      <td>multivariate/CATSv2/CATSv2.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>semi-supervised</td>\n",
       "      <td>True</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>4000000</td>\n",
       "      <td>17</td>\n",
       "      <td>0.047525</td>\n",
       "      <td>200</td>\n",
       "      <td>300</td>\n",
       "      <td>500</td>\n",
       "      <td>5000</td>\n",
       "      <td>29.020503</td>\n",
       "      <td>15.193284</td>\n",
       "      <td>no trend</td>\n",
       "      <td>not_stationary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        train_path  \\\n",
       "collection_name dataset_name                                         \n",
       "CATSv2          CATSv2        multivariate/CATSv2/CATSv2.train.csv   \n",
       "\n",
       "                                                        test_path  \\\n",
       "collection_name dataset_name                                        \n",
       "CATSv2          CATSv2        multivariate/CATSv2/CATSv2.test.csv   \n",
       "\n",
       "                             dataset_type  datetime_index   split_at  \\\n",
       "collection_name dataset_name                                           \n",
       "CATSv2          CATSv2               real            True  1000000.0   \n",
       "\n",
       "                                   train_type  train_is_normal    input_type  \\\n",
       "collection_name dataset_name                                                   \n",
       "CATSv2          CATSv2        semi-supervised             True  multivariate   \n",
       "\n",
       "                               length  dimensions  contamination  \\\n",
       "collection_name dataset_name                                       \n",
       "CATSv2          CATSv2        4000000          17       0.047525   \n",
       "\n",
       "                              num_anomalies  min_anomaly_length  \\\n",
       "collection_name dataset_name                                      \n",
       "CATSv2          CATSv2                  200                 300   \n",
       "\n",
       "                              median_anomaly_length  max_anomaly_length  \\\n",
       "collection_name dataset_name                                              \n",
       "CATSv2          CATSv2                          500                5000   \n",
       "\n",
       "                                   mean     stddev     trend    stationarity  \\\n",
       "collection_name dataset_name                                                   \n",
       "CATSv2          CATSv2        29.020503  15.193284  no trend  not_stationary   \n",
       "\n",
       "                              period_size  \n",
       "collection_name dataset_name               \n",
       "CATSv2          CATSv2                NaN  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.refresh()\n",
    "dm.df().loc[(slice(dataset_collection_name,dataset_collection_name), slice(None))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
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       "      <td>1.679129</td>\n",
       "      <td>0.573972</td>\n",
       "      <td>0.600126</td>\n",
       "      <td>4.674328e+01</td>\n",
       "      <td>-14.161588</td>\n",
       "      <td>0.101711</td>\n",
       "      <td>0.444078</td>\n",
       "      <td>404.386243</td>\n",
       "      <td>-11.330682</td>\n",
       "      <td>34.392976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999996</th>\n",
       "      <td>2023-02-27 20:53:16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>18.995890</td>\n",
       "      <td>-0.506435</td>\n",
       "      <td>0.826429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.662337</td>\n",
       "      <td>0.545274</td>\n",
       "      <td>0.588243</td>\n",
       "      <td>4.661676e+01</td>\n",
       "      <td>-14.161774</td>\n",
       "      <td>0.149931</td>\n",
       "      <td>0.443609</td>\n",
       "      <td>404.997544</td>\n",
       "      <td>-11.303570</td>\n",
       "      <td>34.271856</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999997</th>\n",
       "      <td>2023-02-27 20:53:17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>19.034410</td>\n",
       "      <td>-0.506417</td>\n",
       "      <td>0.826542</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.645714</td>\n",
       "      <td>0.518010</td>\n",
       "      <td>0.576595</td>\n",
       "      <td>4.649056e+01</td>\n",
       "      <td>-14.161971</td>\n",
       "      <td>0.194793</td>\n",
       "      <td>0.443141</td>\n",
       "      <td>405.567265</td>\n",
       "      <td>-11.277001</td>\n",
       "      <td>34.150435</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999998</th>\n",
       "      <td>2023-02-27 20:53:18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>19.168969</td>\n",
       "      <td>-0.506400</td>\n",
       "      <td>0.826655</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.629257</td>\n",
       "      <td>0.492110</td>\n",
       "      <td>0.565177</td>\n",
       "      <td>4.636466e+01</td>\n",
       "      <td>-14.162178</td>\n",
       "      <td>0.235987</td>\n",
       "      <td>0.442674</td>\n",
       "      <td>406.098996</td>\n",
       "      <td>-11.250965</td>\n",
       "      <td>34.028822</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999999</th>\n",
       "      <td>2023-02-27 20:53:19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>19.309191</td>\n",
       "      <td>-0.506383</td>\n",
       "      <td>0.826767</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.612964</td>\n",
       "      <td>0.467504</td>\n",
       "      <td>0.553986</td>\n",
       "      <td>4.623907e+01</td>\n",
       "      <td>-14.162395</td>\n",
       "      <td>0.273255</td>\n",
       "      <td>0.442206</td>\n",
       "      <td>406.595841</td>\n",
       "      <td>-11.225456</td>\n",
       "      <td>33.907112</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000000 rows × 20 columns</p>\n",
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      ],
      "text/plain": [
       "                   timestamp  aimp  amud       arnd     asin1     asin2  adbr  \\\n",
       "0        2023-01-01 00:00:00   0.0   1.0  20.000000  0.000000  0.000000   0.0   \n",
       "1        2023-01-01 00:00:01   0.0   1.0  20.080031  0.000020  0.000200   0.0   \n",
       "2        2023-01-01 00:00:02   0.0   1.0  20.276562  0.000040  0.000400   0.0   \n",
       "3        2023-01-01 00:00:03   0.0   1.0  20.730938  0.000060  0.000600   0.0   \n",
       "4        2023-01-01 00:00:04   0.0   1.0  21.118101  0.000080  0.000800   0.0   \n",
       "...                      ...   ...   ...        ...       ...       ...   ...   \n",
       "4999995  2023-02-27 20:53:15   0.0 -15.0  19.146985 -0.506452  0.826317   0.0   \n",
       "4999996  2023-02-27 20:53:16   0.0 -15.0  18.995890 -0.506435  0.826429   0.0   \n",
       "4999997  2023-02-27 20:53:17   0.0 -15.0  19.034410 -0.506417  0.826542   0.0   \n",
       "4999998  2023-02-27 20:53:18   0.0 -15.0  19.168969 -0.506400  0.826655   0.0   \n",
       "4999999  2023-02-27 20:53:19   0.0 -15.0  19.309191 -0.506383  0.826767   0.0   \n",
       "\n",
       "         adfl      bed1      bed2      bfo1          bfo2       bso1  \\\n",
       "0         0.0  0.000000  0.000000  0.000000  0.000000e+00   0.000000   \n",
       "1         0.0  0.000000  0.000000  0.000000  4.993912e-07   0.000789   \n",
       "2         0.0  0.000000  0.000000  0.000000  1.496957e-06   0.003115   \n",
       "3         0.0  0.000000  0.000000  0.000000  2.991484e-06   0.006914   \n",
       "4         0.0  0.000000  0.000000  0.000000  4.981761e-06   0.012123   \n",
       "...       ...       ...       ...       ...           ...        ...   \n",
       "4999995   0.0  1.679129  0.573972  0.600126  4.674328e+01 -14.161588   \n",
       "4999996   0.0  1.662337  0.545274  0.588243  4.661676e+01 -14.161774   \n",
       "4999997   0.0  1.645714  0.518010  0.576595  4.649056e+01 -14.161971   \n",
       "4999998   0.0  1.629257  0.492110  0.565177  4.636466e+01 -14.162178   \n",
       "4999999   0.0  1.612964  0.467504  0.553986  4.623907e+01 -14.162395   \n",
       "\n",
       "             bso2      bso3        ced1       cfo1       cso1    y  category  \n",
       "0        0.000000  0.000000    0.000000   0.000000   0.000000  0.0       0.0  \n",
       "1        0.000000  0.000000    0.000000   0.000021   0.001229  0.0       0.0  \n",
       "2        0.000000  0.000000    0.000000   0.000104   0.004833  0.0       0.0  \n",
       "3        0.000000  0.000000    0.000000   0.000285   0.010688  0.0       0.0  \n",
       "4        0.000000  0.000000    0.000000   0.000601   0.018669  0.0       0.0  \n",
       "...           ...       ...         ...        ...        ...  ...       ...  \n",
       "4999995  0.101711  0.444078  404.386243 -11.330682  34.392976  0.0       0.0  \n",
       "4999996  0.149931  0.443609  404.997544 -11.303570  34.271856  0.0       0.0  \n",
       "4999997  0.194793  0.443141  405.567265 -11.277001  34.150435  0.0       0.0  \n",
       "4999998  0.235987  0.442674  406.098996 -11.250965  34.028822  0.0       0.0  \n",
       "4999999  0.273255  0.442206  406.595841 -11.225456  33.907112  0.0       0.0  \n",
       "\n",
       "[5000000 rows x 20 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(source_folder / \"data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:split_at, \"y\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[split_at:, \"y\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0., 12.,  1.,  8.,  7.,  9.,  4.,  3.,  5., 11., 10.,  2., 13.,\n",
       "        6.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"category\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>aimp</th>\n",
       "      <th>amud</th>\n",
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       "      <th>1</th>\n",
       "      <td>2023-01-01 00:00:01</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.004833</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-01-01 00:00:03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.010688</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-01-01 00:00:04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.118101</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.012123</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000601</td>\n",
       "      <td>0.018669</td>\n",
       "      <td>False</td>\n",
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       "      <th>4999995</th>\n",
       "      <td>2023-02-27 20:53:15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>19.146985</td>\n",
       "      <td>-0.506452</td>\n",
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       "      <td>404.386243</td>\n",
       "      <td>-11.330682</td>\n",
       "      <td>34.392976</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4999996</th>\n",
       "      <td>2023-02-27 20:53:16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>18.995890</td>\n",
       "      <td>-0.506435</td>\n",
       "      <td>0.826429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.662337</td>\n",
       "      <td>0.545274</td>\n",
       "      <td>0.588243</td>\n",
       "      <td>4.661676e+01</td>\n",
       "      <td>-14.161774</td>\n",
       "      <td>0.149931</td>\n",
       "      <td>0.443609</td>\n",
       "      <td>404.997544</td>\n",
       "      <td>-11.303570</td>\n",
       "      <td>34.271856</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999997</th>\n",
       "      <td>2023-02-27 20:53:17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>19.034410</td>\n",
       "      <td>-0.506417</td>\n",
       "      <td>0.826542</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>405.567265</td>\n",
       "      <td>-11.277001</td>\n",
       "      <td>34.150435</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4999998</th>\n",
       "      <td>2023-02-27 20:53:18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>19.168969</td>\n",
       "      <td>-0.506400</td>\n",
       "      <td>0.826655</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.629257</td>\n",
       "      <td>0.492110</td>\n",
       "      <td>0.565177</td>\n",
       "      <td>4.636466e+01</td>\n",
       "      <td>-14.162178</td>\n",
       "      <td>0.235987</td>\n",
       "      <td>0.442674</td>\n",
       "      <td>406.098996</td>\n",
       "      <td>-11.250965</td>\n",
       "      <td>34.028822</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999999</th>\n",
       "      <td>2023-02-27 20:53:19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>19.309191</td>\n",
       "      <td>-0.506383</td>\n",
       "      <td>0.826767</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.612964</td>\n",
       "      <td>0.467504</td>\n",
       "      <td>0.553986</td>\n",
       "      <td>4.623907e+01</td>\n",
       "      <td>-14.162395</td>\n",
       "      <td>0.273255</td>\n",
       "      <td>0.442206</td>\n",
       "      <td>406.595841</td>\n",
       "      <td>-11.225456</td>\n",
       "      <td>33.907112</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000000 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   timestamp  aimp  amud       arnd     asin1     asin2  adbr  \\\n",
       "0        2023-01-01 00:00:00   0.0   1.0  20.000000  0.000000  0.000000   0.0   \n",
       "1        2023-01-01 00:00:01   0.0   1.0  20.080031  0.000020  0.000200   0.0   \n",
       "2        2023-01-01 00:00:02   0.0   1.0  20.276562  0.000040  0.000400   0.0   \n",
       "3        2023-01-01 00:00:03   0.0   1.0  20.730938  0.000060  0.000600   0.0   \n",
       "4        2023-01-01 00:00:04   0.0   1.0  21.118101  0.000080  0.000800   0.0   \n",
       "...                      ...   ...   ...        ...       ...       ...   ...   \n",
       "4999995  2023-02-27 20:53:15   0.0 -15.0  19.146985 -0.506452  0.826317   0.0   \n",
       "4999996  2023-02-27 20:53:16   0.0 -15.0  18.995890 -0.506435  0.826429   0.0   \n",
       "4999997  2023-02-27 20:53:17   0.0 -15.0  19.034410 -0.506417  0.826542   0.0   \n",
       "4999998  2023-02-27 20:53:18   0.0 -15.0  19.168969 -0.506400  0.826655   0.0   \n",
       "4999999  2023-02-27 20:53:19   0.0 -15.0  19.309191 -0.506383  0.826767   0.0   \n",
       "\n",
       "         adfl      bed1      bed2      bfo1          bfo2       bso1  \\\n",
       "0         0.0  0.000000  0.000000  0.000000  0.000000e+00   0.000000   \n",
       "1         0.0  0.000000  0.000000  0.000000  4.993912e-07   0.000789   \n",
       "2         0.0  0.000000  0.000000  0.000000  1.496957e-06   0.003115   \n",
       "3         0.0  0.000000  0.000000  0.000000  2.991484e-06   0.006914   \n",
       "4         0.0  0.000000  0.000000  0.000000  4.981761e-06   0.012123   \n",
       "...       ...       ...       ...       ...           ...        ...   \n",
       "4999995   0.0  1.679129  0.573972  0.600126  4.674328e+01 -14.161588   \n",
       "4999996   0.0  1.662337  0.545274  0.588243  4.661676e+01 -14.161774   \n",
       "4999997   0.0  1.645714  0.518010  0.576595  4.649056e+01 -14.161971   \n",
       "4999998   0.0  1.629257  0.492110  0.565177  4.636466e+01 -14.162178   \n",
       "4999999   0.0  1.612964  0.467504  0.553986  4.623907e+01 -14.162395   \n",
       "\n",
       "             bso2      bso3        ced1       cfo1       cso1  is_anomaly  \n",
       "0        0.000000  0.000000    0.000000   0.000000   0.000000       False  \n",
       "1        0.000000  0.000000    0.000000   0.000021   0.001229       False  \n",
       "2        0.000000  0.000000    0.000000   0.000104   0.004833       False  \n",
       "3        0.000000  0.000000    0.000000   0.000285   0.010688       False  \n",
       "4        0.000000  0.000000    0.000000   0.000601   0.018669       False  \n",
       "...           ...       ...         ...        ...        ...         ...  \n",
       "4999995  0.101711  0.444078  404.386243 -11.330682  34.392976       False  \n",
       "4999996  0.149931  0.443609  404.997544 -11.303570  34.271856       False  \n",
       "4999997  0.194793  0.443141  405.567265 -11.277001  34.150435       False  \n",
       "4999998  0.235987  0.442674  406.098996 -11.250965  34.028822       False  \n",
       "4999999  0.273255  0.442206  406.595841 -11.225456  33.907112       False  \n",
       "\n",
       "[5000000 rows x 19 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"is_anomaly\"] = df[\"y\"].astype(np.bool_)\n",
    "df = df.drop(columns=[\"y\", \"category\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.iloc[1_004_000:1_006_000, 1:5].plot(subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1005105, 1005106, 1005107, ..., 4982551, 4982552, 4982553])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nonzero(df[\"is_anomaly\"].values)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
